On the validity and applicability of the INFERNO system
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
Uncertainty and vagueness in knowledge based systems
Uncertainty and vagueness in knowledge based systems
Constraint propagation with imprecise conditional probabilities
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
New direction for uncertainty reasoning in deductive databases
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Database Support for Problematic Knowledge
EDBT '92 Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology
Probabilistic Reasoning With Facts And Rules In Deductive Databases
ECSQAU Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
A Hybrid Approach for Modeling Uncertainty in Terminological Logics
ECSQAU Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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The DUCK-calculus presented here is a recent approach to cope with probabilistic uncertainty in a sound and efficient way. Uncertain rules with bounds for probabilities and explicit conditional independences can be maintained incrementally. The basic inference mechanism relies on local bounds propagation, implementable by deductive databases with a bottom-up fixpoint evaluation. In situations, where no precise bounds are deducible, it can be combined with simple operations research techniques on a local scope. In particular, we provide new precise analytical bounds for probabilistic entailment.